American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
Biostatistics Seminar Series


Kay See Tan, Ph.D.
Assistant Attending Biostatistician
Memorial Sloan Kettering Cancer Center

PROPENSITY SCORE METHODS IN THE PRESENCE OF MISSING COVARIATES

Propensity score methods such as nearest-neighbor matching and inverse-probability treatment weighting (IPTW) have been proposed as a means to recover balance between groups of interest and mimic the sample that would have been observed in a randomized trial. When estimating propensity scores, missing covariate data is a major issue that is commonly overlooked, leading to suboptimal matching or improper IPTW. Multiple imputation (MI) is a natural procedure to handle missing data in this context. However, there are open issues regarding the implementation of MI for propensity score analysis. In this talk, we will (1) investigate two opposing proposed methods to combine the MI and propensity score analysis steps, and (2) address variance estimation of the IPTW estimators after MI.


Date: Wednesday, April 3, 2019
Time: 4:00 - 5:00 P.M.
Location: Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
485 Lexington Avenue
(Between 46th & 47th Streets)
2nd Floor, Conference Room B
New York, New York

**Outside visitors please email vasques2@mskcc.org for building access. You must be on the security list to enter the floor.

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